As shown in Table 1 in the experiments section, without RL, training under MLE already produces high-quality candidate captions with the upper bound reaching a CIDEr-D score of \(141.9\)  points. We suggest that one possible future work is training a stronger image-text selector, taking the progress of cross-modal representations beyond the task of image captioning, such as visual question answering, visual reasoning, and image-text retrieval [1]}. In this way, we may quickly train a generator without RL and fine-tune an image-text selector based on pre-trained cross-modal embedding networks. This paradigm may also bridge the gap between text generation and image/text understanding tasks.
